An automatic graph layout procedure to visualize correlated data

This paper introduces an automatic procedure to assist on the interpretation of a large dataset when a similarity metric is available. We propose a visualization approach based on a graph layout method- ology that uses a Quadratic Assignment Problem (QAP) formulation. The methodology is presented using as testbed a time series dataset of the Standard & Poor’s 100, one the leading stock market indicators in the United States. A weighted graph is created with the stocks repre- sented by the nodes and the edges’ weights are related to the correlation between the stocks’ time series. A heuristic for clustering is then pro- posed; it is based on the graph partition into disconnected subgraphs allowing the identification of clusters of highly-correlated stocks. The final layout corresponds well with the perceived market notion of the different industrial sectors. We compare the output of this procedure with a traditional dendogram approach of hierarchical clustering IFIP International Conference on Artificial Intelligence in Theory and Practice - Knowledge Acquisition and Data Mining Red de Universidades con Carreras en Informática (RedUNCI)